code: def _make_branch(use_pool, norm_layer, norm_kwargs, *conv_settings):
      out = nn.HybridSequential(prefix="")
      if use_pool == "avg":
          out.add(nn.AvgPool2D(pool_size=3, strides=1, padding=1))
      elif use_pool == "max":
          out.add(nn.MaxPool2D(pool_size=3, strides=2))
      setting_names = ["channels", "kernel_size", "strides", "padding"]
      for setting in conv_settings:
          kwargs = {}
          for i, value in enumerate(setting):
              if value is not None:
                  kwargs[setting_names[i]] = value
          out.add(_make_basic_conv(norm_layer, norm_kwargs, **kwargs))
      return out
  
file_path: gluon-cv/gluoncv/model_zoo/inception.py
imports: from mxnet.context import cpu
  from mxnet.gluon.block import HybridBlock
  from mxnet.gluon import nn
  from mxnet.gluon.nn import BatchNorm
  from mxnet.gluon.contrib.nn import HybridConcurrent
label_desc: Use image node_id 2 for calling a global function with example usage: _make_branch(use_pool, norm_layer, norm_kwargs) and returns: out